Current systems for tracking moving targets are advancing from being totally dependent on a human operator to identify and follow the targets, to being automated aids that can identify and track the targets, with a final goal of being fully automated. These target tracking systems may rely, for example, on the use of sonar or radar.
In general, current target tracking systems can be categorized as either: passive systems or active systems. Passive systems may utilize passive receivers to acquire signals emitted from possible targets. Active systems may utilize one or more transmitters to produce a signal (described as a “ping”) and one or multiple receivers to listen for reflections of the signal/ping from possible targets. If the transmitter and receiver are co-located the system is described as monostatic. If the transmitter and receiver are not co-located, the system is described as bistatic. When multiple transmitters or receivers are used, the system is described as multistatic.
Some examples of known techniques used for multi-target tracking are: (i) Maximum Likelihood (ML) probabilistic Data Association (PDA) (MLPDA); (ii) Multiple Hypothesis Tracking (MHT) and its variants such as, for example, Probabilistic MHT (PMHT), Maximum Likelihood PMHT, Distributed MHT; (iii) Probability Hypothesis Density (PHD) tracking and its variants such as, for example, Gaussian Mixture Cardinalized PHD; (iv) Particle Filtering (PF) and its variants.
Excluding the PF-based techniques, each of the foregoing techniques rely upon a linear Gaussian assumption and are thus not optimal for multiple target tracking applications or circumstances that do not follow these assumptions. PF-based techniques, on the other hand, are capable of dealing with nonlinear/non-Gaussian assumptions, making them more suitable for dealing with arbitrary sensor characteristics, motion dynamics and noise distributions. However, the known PF-based techniques also suffer from certain shortcomings regarding the problem of multiple target tracking. For instance, in some currently available systems, PF-based techniques are limited to only being capable of tracking a single target at a time (i.e. not multiple targets). The technique may be augmented to other techniques to allow for multiple-target tracking, such as, for example, by utilizing hierarchical data fusion systems, in which the sensor-level tracking may be performed using PF, and the central-level track fusion may be performed using a technique relying on a Gaussian model. Despite having improved performance at the sensor level, however, such adapted systems can still suffer from loss of performance if the sensor level tracks are fused using a Gaussian model. In order to avoid this, the overall technique should not need to rely, in any way, upon linear or Gaussian assumptions. Thus, the technique should rely on PF-based methods only.
As mentioned earlier the basic PF-based techniques can track only one target. One result of the foregoing limitations is that, where the PF-based technique is utilized to track multiple targets, the sample set of particles will tend to only track or follow one target over time, which will likely be the target that has the most persistent and/or consecutive measurements. Several attempts to overcome this have been made, including, for example, modifying the PF-based system.
For instance, some PF-based techniques have been modified to track a constant, previously-determined number of targets by augmenting the state vector used by the PF to include all the states for the known number of targets, while other techniques account for a previously unknown number of targets by estimating the number of targets and making the overall state vector size variable. Notwithstanding that the former approach is highly impractical in real life scenarios, neither approach has the capacity to tackle the initialization of new targets entering the surveillance area later in the scenario based on the observation sets.
Another approach of modifying known PF-based techniques has been to adapt the PF-based techniques to choose a large number of targets to constitute the state vector and to provide indicators of the targets that are considered to be “alive” (i.e. actual tracked targets). This approach is computationally expensive, however, because of the very large state vector.
Other approaches have modified the PF-based techniques to render them capable of tracking a random number of actual targets having a very small upper bound for the number targets tracked, however this approach is limited in its ability to cope with applications having a number of targets larger than the upper bound.
Finally, another approach of modifying known PF-based techniques is to render the technique capable of tracking an unknown number of targets by applying a separate filter (i.e. an additional PF) for each detected target. This solution is computationally expensive, and may not be feasible for performing online tracking.
There is therefore a need for a method of dealing with nonlinear/non-Gaussian systems (i.e. can deal with arbitrary sensor characteristics, motion dynamics, and noise distributions) that can be used for tracking an unknown and time-varying number of targets, without constraint on the number of targets tracked nor on the time of their appearance in the surveillance area. Such a method may be utilized in circumstances, such as for example:                having a high number of false contacts (i.e. high number of false measurements that are not related to a reflection from an actual target but rather to, for example, noise reaching the receiver);        having multiple reflections reaching the receivers, among which the true target reflections might be buried (such scenarios can arise, for example, in active multistatic systems); or        having high possibilities of temporarily missing the true target contacts while still being able to keep continuous target tracks (such scenarios can arise, for example, in passive systems).        